Lecture 1: Introduction

Lecture 2: Counterfactual Framework and Randomized Controlled Trials

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  Counterfactual Framework

  
  


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  Randomized Controlled Trials

  
  

Lecture 3: Regression and Matching

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  Review for Linear Regression

  
  


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  Omitted Variable Bias

  
  


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  The Payoff to Attending a More Selective College

  
  

Lecture 4: Machine Learning and High Dimensional Data

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  Bias-Variance Decomposition

  
  


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  Cross-Validation

  
  


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  Shrinkage Methods

  
  

Midterm Presentation (4/10)

Lecture 5: Difference-in-Differences

Lecture 6: Synthetic Control Method

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  Difference-in-Differences vs. Synthetic Control Method

  
  


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  Minimum Wage and Mariel Boatlift

  
  

Lecture 7: Instrumental Variable 

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  Instrumental Variables

  
  


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  Two-Stage Least Squares

  
  


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  LATE

  
  

Lecture 8: Regression Discontinuity

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  Sharp RD

  
  


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  Fuzzy RD and IV

  
  

Final Presentations (5/15)

There will be 2-3 problem sets, two presentations, and a research proposal. The course grade will be determined by:

1. 50% Problem Sets 

We will do four problem sets during the course of semester. The problem set questions will include both theoretical questions and statistical analysis of data, which will involve the software package STATA (or R).

2. 50% Paper Proposal:

Each student is required to write a high-quality paper proposal. You should arrange to meet with me when you are ready to talk about a topic for your paper, and brief outlines (less than one page) of proposals are due before midterm presentations. Final proposals are due on June 5.

Text:

Joshua D. Angrist and JÖrn-Steffen Pischke (2014), Mastering 'Metrics, Princeton University Press 

Jeffrey Wooldridge (2015), Introductory Econometrics: A Modern Approach (6th edition), Cengage 

Gareth James, Daniela Witten, Trevor Hastie and Robert Tibsharani (2013), An Introduction to Statistical Learning: with Applications in R, Springer. http://www-bcf.usc.edu/~gareth/ISL/